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Crowdsourcing

About: Crowdsourcing is a research topic. Over the lifetime, 12889 publications have been published within this topic receiving 230638 citations.


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Journal ArticleDOI
TL;DR: This study presents an initial attempt to explore and understand online social behavior in crowdsourcing communities, with the insights from both plural subject theory and commitment-trust theory, and indicates that, although both I-mode and the authors-mode collective intentions significantly predicted online socialbehavior in wiki communities, they- mode collective intention exerted a greater effect on users' behavior.

73 citations

Journal ArticleDOI
TL;DR: A survey of citizen science project leaders is presented, identifying sub-groups of project types according to a variety of features related to project design and management, including funding sources, goals, participant activities, data quality processes, and social interaction.
Abstract: Citizen science has seen enormous growth in recent years, in part due to the influence of the Internet, and a corresponding growth in interest. However, the few stand-out examples that have received attention from media and researchers are not representative of the diversity of the field as a whole, and therefore may not be the best models for those seeking to study or start a citizen science project. In this work, we present the results of a survey of citizen science project leaders, identifying sub-groups of project types according to a variety of features related to project design and management, including funding sources, goals, participant activities, data quality processes, and social interaction. These combined features highlight the diversity of citizen science, providing an overview of the breadth of the phenomenon and laying a foundation for comparison between citizen science projects and to other online communities.

73 citations

Proceedings Article
28 Jun 2013
TL;DR: To the knowledge, this is the first empirical evidence that dynamic networks of mobile individuals are highly navigable and shows that packages can be delivered with remarkable speed and coverage.
Abstract: Research on human computation and crowdsourcing has concentrated on tasks that can be accomplished remotely over the Internet. We introduce a general class of problems we call crowdphysics (CP)---crowdsourcing tasks that require people to collaborate and synchronize both in time and physical space. As an illustrative example, we focus on a crowd-powered delivery service---a specific CP instance where people go about their daily lives, but have the opportunity to carry packages to be delivered to specific locations or individuals. Each package is handed off from person to person based on overlaps in time and space until it is delivered. We formulate CP tasks by reduction to a graph-planning problem, and analyze the performance using a large sample of geotagged tweets as a proxy for people's location. We show that packages can be delivered with remarkable speed and coverage. These results hold for the case when we know people's future locations and also when routing without global knowledge, making only local greedy decisions. To our knowledge, this is the first empirical evidence that dynamic networks of mobile individuals are highly navigable.

73 citations

Proceedings Article
01 Jan 2014
TL;DR: This work describes the platform AIDR (Artificial Intelligence for Disaster Response), which collects human annotations over time to create and maintain automatic supervised classifiers for social media messages, and studies two significant challenges in its design.
Abstract: An emerging paradigm for the processing of data streams involves human and machine computation working together, allowing human intelligence to process large-scale data. We apply this approach to the classification of crisis-related messages in microblog streams. We begin by describing the platform AIDR (Artificial Intelligence for Disaster Response), which collects human annotations over time to create and maintain automatic supervised classifiers for social media messages. Next, we study two significant challenges in its design: (1) identifying which elements must be labeled by humans, and (2) determining when to ask for such annotations to be done. The first challenge is selecting the items to be labeled by crowdsourcing workers to maximize the productivity of their work. The second challenge is to schedule the work in order to reliably maintain high classification accuracy over time. We provide and validate answers to these challenges by extensive experimentation on realworld datasets.

73 citations

Proceedings ArticleDOI
Yueming Wei1, Yanmin Zhu1, Hongzi Zhu1, Qian Zhang1, Guangtao Xue1 
01 Apr 2015
TL;DR: A general framework for the design of truthful online double auctions for dynamic mobile crowdsourcing is proposed and it is demonstrated that the proposed auctions are strategy-proof, individual rational, and ensure budget balance.
Abstract: Stimulating both service users and service providers is of paramount importance to mobile crowdsourcing A few incentive mechanisms have been proposed, but all of them have focused only on one-sided interactions either among service users or among service providers For the first time, to the best of our knowledge, we investigate the important two-sided online interactions among service users and service providers in mobile crowdsourcing We model such interactions as online double auctions, explicitly taking the dynamic nature of both users and providers into account We propose a general framework for the design of truthful online double auctions for dynamic mobile crowdsourcing The framework is expressive and can work with different price schedules We propose price-ranked online double auctions with four price schedules to implement the framework, which are suitable for different scenarios With theoretical analysis and extensive simulations we demonstrate that the proposed auctions are strategy-proof, individual rational, and ensure budget balance

73 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023637
20221,420
2021996
20201,250
20191,341
20181,396